Why Normalize Data?
Data normalization is paramount for any successful database design. For prior to data analysis, we must first query databases. And should formats be random and schemas poor quality, searching for our answers can be far more time-intensive than necessary. Normalization is therefore key for business analysts and data scientists who:
- Collect, review, manage, and organize data.
- Perform complex statistical modeling on data.
And benefits anyone responsible for:
- The accuracy and completeness of data received and entered
- Database maintenance and performance
- Ensuring data are ready for daily, weekly, monthly, quarterly, or yearly reporting
- Indexing system processes
- Assessing business models
- Implementing technical integrations.
To wit, normalization helps you to:
- Explain robust relationships between non-linear datasets. Databases are far more useful when data formats are clean — or less random and more predictable. Transforming inputs “conditions” data so analysts may accurately compare, and even superimpose, datasets with different distributions.
- Converge weight and bias prior to a multivariate analysis. Normalizations prevents outliers — variables with a wider range of values — from outweighing other variables.
- Accelerate results. Neural and deep learning networks, for instance, apply calculations on tons of data. Time is of the essence.
In short, with normalization, data are more accurate, faster to load, more efficient to access, and produce the results you’re searching for.
What is Data Normalization?
More specifically, normalization is the process of:
- Formatting data so all names, units, and scales can be processed in a visualization or analytics system.
- Getting rid of duplicates
- Resolving conflicts
- Consolidating data
Different levels of normalization exist. Each to some degree demand an understanding about how platforms store and share data.
There are 1st, 2nd, and 3rd normal forms (NF). The higher the normal form, the greater the depth of normalization.
Each normal form, to its own degree, asks whether a key is “prime” or “non-prime”. Prime means any key attribute is a member of candidate key. Nonprime refers to a normal attribute that is not a member of any candidate key.
1st Normal Form
1st NF is satisfied when an entity type contains:
- No repeating groups or duplicate arrays of data in the table.
- Each cell is single-valued or atomic.
2nd Normal Form
2nd NF is satisfied when:
- 1st NF is satisfied.
- All non-key or non-prime attributes must fully depend on their primary key.
3rd Normal Form
3rd NF is satisfied when:
- 2nd NF is satisfied.
- There are no transitive dependencies; all attributes depend directly on the primary key.
Extract, Transform, Load
Aligning the names, units, and scales of different data sources ensures that the information is available to the current visualization system. This alignment, this process by which data are normalized, is achieved during the Extract Transform Load process, or ETL.
That a business would need to transform data in order to store it into the warehouse means there could be a significant delay before the data would be usable. Transforming the data within the warehouse provides faster access to that data.
ETL effectively pulls data from one source, modifies the data, then loads the data in a specified destination.
Aligning Data Across Multiple Data Sets
To align data across multiple data sets, you must clean the data by mapping, merging, and matching records.
A master normalization map is vital to your virtual data pipeline. Auto-match custom fields help to facilitate which records refer to related fields in other tables.
Based on a “hard match” on a de-dupe field, merges records. For example, contacts: email Address; companies: company name.
Sometime there are “fuzzy” or close matches, likely misspellings, leading spaces, and abbreviations with standard meanings (e.g. GE = General Electric). With standards and controls, you can more readily resolve matches.
For instance, say you wish to search for a match on [Nor’easter], but the database contains [Nor easter] (sans apostrophe). Any match search for the record will fail, despite that the meanings between the two fields refer to the same definition. A standard here would be to match on fields with or without apostrophes that contain an otherwise identical spelling.
Normalization also helps maintain relationships by reading related objects. Including:
- Roll-up fields with SQL queries with pre-built functions.
- 1:1 Relationships
- 1:Many Relationships
- Use most recent value
- Use most ‘advanced’ value (e.g. opportunity stage)
- Equations (e.g sum, etc.)
Our default for handling conflicts is the “last modified”. They will be able to get to system of record via SQL query.
When imposing consistent standards on your data, there are an endless number of fields for which you could update formats. Here are a few salient examples:
- Phone Number (603-306-0360 vs. 603.306.0360)
- Date Formats (DD/MM/YYYY vs. MM/DD/YYYY)
- Numbers (0.5 vs. .5)
- States (NH vs. N.H. vs New Hampshire)
- Countries (codes vs. words)
- Lookup Fields: Labels vs GUIDs
- Single-Select: Labels vs. IDs
- Multi-Select: Labels vs IDs (handling appending values or removing)
- Boolean Fields: true/false; (some systems provide null, others provide nothing if the field has never been updated, so reducing booleans to true/false is ideal)
- Capitalization (Boston vs. boston -- helps for de-duplicating)
The Power of Normalized Data
Consolidating data is a big step. An undertaking that requires time, planning, and expertise in database architecture.
With a process in place, and now that data are arranged in logical groupings — devoid of duplicates, conflicts, and random formats — you now have the power to:
- Bring clarity to your business rules and terminology
- Answer key business questions that involve multiple data sources. (For example, the number customers who spent M on your products and opened N support tickets within 90 days might require data from Salesforce & Zendesk. Now you can answer this business question fast and trust its accuracy.)
- Glean 1:1, 1:many, and even many:many relationships which give you a 360-degree view of your customer
- Visualize data on any of your analytics, BI tools, reports, and dashboards
- Modify data quickly, in one place, without compromising data integrity
- Export logs from one place without any repeats
- Decrease storage requirements
- Query and join customer data from multiple SaaS applications
- And much more.
Our Fusion product helps you automation the normalization of your customer data across multiple application and data sourcesTry Fusion for free to see for yourself, or learn more here.